• Klíčová slova
• čakry,
• MeSH
• holistické zdraví MeSH
• komplementární terapie MeSH
• přenos energie MeSH
• Publikační typ
• příručky MeSH

• Konspekt
• Fyzioterapie. Psychoterapie. Alternativní lékařství
• NLK Obory
• alternativní lékařství

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

• MeSH
• analýza hlavních komponent * MeSH
• bipolární porucha * diagnostické zobrazování   farmakoterapie   patologie   MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• mladý dospělý MeSH
• mozek diagnostické zobrazování   patologie   MeSH
• mozková kůra diagnostické zobrazování   patologie   MeSH
• obezita * diagnostické zobrazování   MeSH
• schizofrenie diagnostické zobrazování   patologie   farmakoterapie   patofyziologie   MeSH
• shluková analýza MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: The most common causes of death in schizophrenia are cardiovascular disorders, which are closely related to metabolic syndrome/obesity. To better understand the development of metabolic alterations early in the course of illness, we quantified daily medication exposure in the first days of the first hospitalization for psychosis and related it to changes in weight and metabolic markers. STUDY DESIGN: We recruited participants with first episode psychosis (FEP, N = 173) during their first psychiatric hospitalization and compared them to controls (N = 204). We prospectively collected weight, body mass index, metabolic markers, and exact daily medication exposure at admission and during hospitalization. STUDY RESULTS: Individuals with FEP gained on average 0.97 ± 2.26 BMI points or 3.46 ± 7.81 kg of weight after an average of 44.6 days of their first inpatient treatment. Greater antipsychotic exposure was associated with greater BMI increase, but only in people with normal/low baseline BMI. Additional predictors of weight gain included type of medication and duration of treatment. Medication exposure was not directly related to metabolic markers, but higher BMI was associated with higher TGC, TSH, and lower HDL. Following inpatient treatment, participants with FEP had significantly higher BMI, TGC, prolactin, and lower fT4, HDL than controls. CONCLUSION: During their first admission, people with FEP, especially those with normal/low baseline BMI, showed a rapid and clinically significant weight increase, which was associated with exposure to antipsychotics, and with metabolic changes consistent with metabolic syndrome. These findings emphasize weight monitoring in FEP and suggest a greater need for caution when prescribing metabolically problematic antipsychotics to people with lower BMI.

• MeSH
• antipsychotika * škodlivé účinky   MeSH
• hmotnostní přírůstek MeSH
• hospitalizace MeSH
• lidé MeSH
• metabolický syndrom * chemicky indukované   epidemiologie   MeSH
• psychotické poruchy * farmakoterapie   MeSH
• schizofrenie * farmakoterapie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Insulin-sensitizing medications were originally used in psychiatric practice to treat weight gain and other metabolic side effects that accompany the use of mood stabilizers, antipsychotics, and some antidepressants. However, in recent studies these medications have been shown to cause improvement in depressive symptoms, creating a potential new indication outside of metabolic regulation. However, it is still unclear whether the antidepressant properties of these medications are associated with improvements in metabolic markers. We performed a systematic search of the literature following PRISMA guidelines of studies investigating antidepressant effects of insulin-sensitizing medications. We specifically focused on whether any improvements in depressive symptoms were connected to the improvement of metabolic dysfunction. Majority of the studies included in this review reported significant improvement in depressive symptoms following treatment with insulin-sensitizing medications. Nine out of the fifteen included studies assessed for a correlation between improvement in symptoms and changes in metabolic markers and only two of the nine studies found such association, with effect sizes ranging from R2 = 0.26-0.38. The metabolic variables, which correlated with improvements in depressive symptoms included oral glucose tolerance test, fasting plasma glucose and glycosylated hemoglobin following treatment with pioglitazone or metformin. The use of insulin-sensitizing medications has a clear positive impact on depressive symptoms. However, it seems that the symptom improvement may be unrelated to improvement in metabolic markers or weight. It is unclear which additional mechanisms play a role in the observed clinical improvement. Some alternative options include inflammatory, neuroinflammatory changes, improvements in cognitive functioning or brain structure. Future studies of insulin-sensitizing medications should measure metabolic markers and study the links between changes in metabolic markers and changes in depression. Additionally, it is important to use novel outcomes in these studies, such as changes in cognitive functioning and to investigate not only acute, but also prophylactic treatment effects.

• MeSH
• antidepresiva škodlivé účinky   MeSH
• antipsychotika * škodlivé účinky   MeSH
• inzulin MeSH
• metformin * terapeutické užití   MeSH
• pioglitazon MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.

• MeSH
• bipolární porucha diagnostické zobrazování   farmakoterapie   patologie   MeSH
• genetika MeSH
• hipokampus diagnostické zobrazování   účinky léků   patologie   MeSH
• lidé MeSH
• magnetická rezonanční tomografie * MeSH
• neurozobrazování * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• metaanalýza MeSH
• multicentrická studie MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

• MeSH
• bipolární porucha * diagnostické zobrazování   patologie   MeSH
• lidé MeSH
• magnetická rezonanční tomografie * MeSH
• metaanalýza jako téma MeSH
• mozková kůra * diagnostické zobrazování   patologie   MeSH
• multicentrické studie jako téma MeSH
• neurozobrazování * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH
• Research Support, N.I.H., Extramural MeSH
• Research Support, N.I.H., Intramural MeSH

Schizophrenia is frequently associated with obesity, which is linked with neurostructural alterations. Yet, we do not understand how the brain correlates of obesity map onto the brain changes in schizophrenia. We obtained MRI-derived brain cortical and subcortical measures and body mass index (BMI) from 1260 individuals with schizophrenia and 1761 controls from 12 independent research sites within the ENIGMA-Schizophrenia Working Group. We jointly modeled the statistical effects of schizophrenia and BMI using mixed effects. BMI was additively associated with structure of many of the same brain regions as schizophrenia, but the cortical and subcortical alterations in schizophrenia were more widespread and pronounced. Both BMI and schizophrenia were primarily associated with changes in cortical thickness, with fewer correlates in surface area. While, BMI was negatively associated with cortical thickness, the significant associations between BMI and surface area or subcortical volumes were positive. Lastly, the brain correlates of obesity were replicated among large studies and closely resembled neurostructural changes in major depressive disorders. We confirmed widespread associations between BMI and brain structure in individuals with schizophrenia. People with both obesity and schizophrenia showed more pronounced brain alterations than people with only one of these conditions. Obesity appears to be a relevant factor which could account for heterogeneity of brain imaging findings and for differences in brain imaging outcomes among people with schizophrenia.

• MeSH
• depresivní porucha unipolární * MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   MeSH
• mozek MeSH
• obezita MeSH
• schizofrenie * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

BACKGROUND: Obesity is highly prevalent in schizophrenia, with implications for psychiatric prognosis, possibly through links between obesity and brain structure. In this longitudinal study in first episode of psychosis (FEP), we used machine learning and structural magnetic resonance imaging (MRI) to study the impact of psychotic illness and obesity on brain ageing/neuroprogression shortly after illness onset. METHODS: We acquired 2 prospective MRI scans on average 1.61 years apart in 183 FEP and 155 control individuals. We used a machine learning model trained on an independent sample of 504 controls to estimate the individual brain ages of study participants and calculated BrainAGE by subtracting chronological from the estimated brain age. RESULTS: Individuals with FEP had a higher initial BrainAGE than controls (3.39 ± 6.36 vs 1.72 ± 5.56 years; β = 1.68, t(336) = 2.59, P = .01), but similar annual rates of brain ageing over time (1.28 ± 2.40 vs 1.07±1.74 estimated years/actual year; t(333) = 0.93, P = .18). Across both cohorts, greater baseline body mass index (BMI) predicted faster brain ageing (β = 0.08, t(333) = 2.59, P = .01). For each additional BMI point, the brain aged by an additional month per year. Worsening of functioning over time (Global Assessment of Functioning; β = -0.04, t(164) = -2.48, P = .01) and increases especially in negative symptoms on the Positive and Negative Syndrome Scale (β = 0.11, t(175) = 3.11, P = .002) were associated with faster brain ageing in FEP. CONCLUSIONS: Brain alterations in psychosis are manifest already during the first episode and over time get worse in those with worsening clinical outcomes or higher baseline BMI. As baseline BMI predicted faster brain ageing, obesity may represent a modifiable risk factor in FEP that is linked with psychiatric outcomes via effects on brain structure.

• MeSH
• dospělí MeSH
• index tělesné hmotnosti MeSH
• lidé MeSH
• longitudinální studie MeSH
• magnetická rezonanční tomografie MeSH
• mladiství MeSH
• mladý dospělý MeSH
• obezita komplikace   diagnostické zobrazování   patologie   patofyziologie   MeSH
• předčasné stárnutí diagnostické zobrazování   etiologie   patologie   patofyziologie   MeSH
• progrese nemoci * MeSH
• psychotické poruchy diagnostické zobrazování   patologie   patofyziologie   MeSH
• rizikové faktory MeSH
• strojové učení * MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

BACKGROUND: Cognitive impairment contributes to deterioration in social, family and work functioning in Bipolar Disorder (BD). Cognitive deficits are present not only during, but also outside of mood episodes. Insulin resistance (IR) impairs cognitive functioning and is frequent in participants with BD. Thus, we hypothesized that IR might contribute to cognitive deficits in remitted BD participants. METHODS: We acquired biochemical (fasting insulin, glucose, lipids) cognitive (California Verbal Learning Test, Digit Span) measures from 100 euthymic participants with BD type I or II. IR was diagnosed using HOMA-IR. RESULTS: BD participants with IR displayed worse composite verbal memory score (-0.38 vs 0.17; F(1, 8.23)=17.90; p = 0.003), while composite working memory scores were comparable in patients with or without IR (-0.20 vs 0.07; F(1, 6.05)=1.64; p = 0.25). Insulin resistance remained significantly associated with composite verbal memory scores (F(1, 47.99)=9.82, p = 0.003) even when we controlled for levels of lipids. The association between IR and verbal memory was not confounded by exposure to antipsychotics, which were not associated with worse cognitive performance (F(1, 2.07)=5.95, p = 0.13). LIMITATIONS: The main limitation is the cross-sectional design, which does not allow us to rule out reverse causation. CONCLUSIONS: We demonstrated that among remitted BD participants without diabetes mellitus, IR was significantly associated with verbal memory performance, even when we controlled for other relevant metabolic or treatment variables. These findings raise the possibility that early detection and treatment of IR, which is reversible, could possibly improve cognitive functioning in at least some BD participants.

• MeSH
• bipolární porucha * komplikace   MeSH
• inzulinová rezistence * MeSH
• lidé MeSH
• neuropsychologické testy MeSH
• paměť MeSH
• poruchy paměti MeSH
• průřezové studie MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

BACKGROUND: Seasonal peaks in hospitalizations for mood disorders and schizophrenia are well recognized and often replicated. The within-subject tendency to experience illness episodes in the same season, that is, seasonal course, is much less established, as certain individuals may temporarily meet criteria for seasonal course purely by chance. AIMS: In this population, prospective cohort study, we investigated whether between and within-subject seasonal patterns of hospitalizations occurred more frequently than would be expected by chance. METHODS: Using a compulsory, standardized national register of hospitalizations, we analyzed all admissions for mood disorders and schizophrenia in the Czech Republic between 1994 and 2013. We used bootstrap tests to compare the observed numbers of (a) participants with seasonal/regular course and (b) hospitalizations in individual months against empirical distributions obtained by simulations. RESULTS: Among 87 184 participants, we found uneven distribution of hospitalizations, with hospitalization peaks for depression in April and November (X2 (11) = 363.66, P < .001), for mania in August (X2 (11) = 50.36, P < .001) and for schizophrenia in June (X2 (11) = 70.34, P < .001). Significantly more participants than would be expected by chance, had two subsequent rehospitalizations in the same 90 days in different years (7.36%, bootstrap P < .01) or after a regular, but non-seasonal interval (6.07%, bootstrap P < .001). The proportion of participants with two consecutive hospitalizations in the same season was below chance level (7.06%). CONCLUSIONS: Psychiatric hospitalizations were unevenly distributed throughout the year (cross-sectional seasonality), with evidence for regularity, but not seasonality of hospitalizations within subjects. Our data do not support the validity of seasonal pattern specifier. Season may be a general risk factor, which increases the risk of hospitalizations across psychiatric participants.

• MeSH
• bipolární porucha * MeSH
• hospitalizace MeSH
• lidé MeSH
• poruchy nálady epidemiologie   MeSH
• prospektivní studie MeSH
• průřezové studie MeSH
• roční období MeSH
• schizofrenie * epidemiologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH